Method and apparatus for selective deep scan using a motion sensor co-located with RFID reader

ABSTRACT

A radio frequency identification (RFID) reader is disclosed that allows for selective deep scans of RFID tags. The RFID reader may include a motion detection sensor, a radio frequency transceiver configured to transmit RF energy at first and second frequencies, and a processor. The processor may perform the following steps: (1) when motion is detected by the sensor, commanding the transceiver to transmit RF energy at the first RFID profile; and (2) when motion has not been detect for a predetermined time period, commanding the transceiver to transmit RF energy at the second RFID profile. A method for using the RFID reader to perform selective deep scanning is also disclosed.

CLAIM OF PRIORITY

This application claims priority to U.S. provisional application No.62/300,637, entitled “MOTION SENSOR CO-LOCATED WITH RFIDREADER/PROBABILISTIC INVENTORY APPLICATION FOR RFID READER”, which wasfiled on Feb. 26, 2016, and U.S. provisional application No. 62/369,488,entitled “AUTONOMOUS ALGORITHMS TO CONTROL A NETWORK OR RFID READERS FOROPTIMIZED ASSET TRACKING AND INVENTORY MANAGEMENT”, which was filed onAug. 1, 2016, the contents of both are incorporated herein by reference.

TECHNICAL FIELD

This invention relates to a radio frequency identification (RFID)systems. More particularly, the invention relates to a method to improvethe performance of RFID systems and to more refine inventory predictionmodels for such systems.

BACKGROUND

Radio-frequency identification (RFID) is a ubiquitous technology thatemploys electromagnetic fields to automatically identify and track tagsthat contain electronically stored information. Passive RFID tagscollect energy from a nearby RFID reader's interrogating radio-frequency(RF) signal to report the electronic data stored within the tag. Thedata may include a unique electronic product code (EPC) that can beassociated with the characteristics of the object to which the tag isconnected.

Because RFID tags use radio-frequency (RF) signals they need not bewithin the line of sight of the RFID reader, an advantage over opticalbarcode technology. RFID tags may thus be embedded in the tracked objector object packaging.

RFID tags are used throughout many industries, including retail,equipment manufacturing, and equipment rental facilities to name a few.For example, a manufacturing plant may embed an RFID tag on a component,and can track that component throughout the manufacturing process, andthe distribution and sale process thereafter. Banks use RFID tagsattached to cash to track the flow money, and such tags are incorporatedinto the uniform product code (UPC) labels on clothing. Ranchers mayimplant RFID tags into their animals to track the movement of theirherds.

RFID may also be used as part of an inventory management system. Areader may send out an interrogation RF signal, then for each tag thatreports back, the system can flag the associated item as remaining ininventory. Those tags that do not report back are flagged as not ininventory.

When several RFID tags are located within a confined volume, it can bedifficult or impossible to interrogate them all. For example, awarehouse may have thousands of tagged items, some of which may be onthe fringe of the RFID reading area, while others may be blocked fromthe interrogating RF signal, preventing their successful interrogationby the RFID reader. Conventional RFID inventory technology willincorrectly flag these non-interrogated tags (and their associatedobjects) as not in inventory. This false reporting leads to costly lostsales, over-ordering, and over-stocking, frustrating the purposes ofRFID inventory management. Accordingly, improvements to existing RFIDsystems are needed to more accurately detect and report taggedinventory.

SUMMARY

The present invention(s) elegantly overcome many of the drawbacks ofprior systems and provide numerous additional improvements and benefitsas will be apparent to persons of skill in the art. Provided in variousexample embodiments is a probabilistic inventory application for an RFIDsystem that provides a more accurate prediction of a facility'sinventory. Additional structures, systems, and methods are also providedfor improved deep scanning of RFID tags throughout a facility to improvethe accuracy of inventory reporting.

Turning first to probabilistic inventory applications for RFID readers,provided in various example embodiments is a radio frequencyidentification (RFID) system for monitoring a space. Such systems mayinclude one or more controllers each connected to one or more RFIDreaders, where each reader can detect data signals from a plurality ofRFID tags attached with objects. The objects may be part of an objectcategory. Example systems may also have a database connected to thecontroller, which may record, for example: (1) a time when the datasignal is received from each tag in the plurality; and (2) the datacontained in the data signal from each tag in the plurality. Examplesystems may include a processor that can perform an inventory analysisof the objects based on a confidence probability curve. For example, inone embodiment this curve may be a decaying function that is based onvarious probabilities.

In various example embodiments the system may also create a contra-EPC.For example, at least one of the plurality of RFID readers may comprisean exit RFID reader used in a point-of-sale (POS) system that recordsthe sale of an object in a database. A processor may be connected to thePOS system to perform the following steps, for example: (1) when salesinformation is received from the POS system, monitoring the exit RFIDreader for detection of data signal from a RFID tag in the plurality,and if an RFID tag is so detected, matching the sales information to theRFID tag and updating the database; and (2) if an RFID tag is not sodetected, creating a contra-EPC associated with the object and updatingthe database.

In various example embodiments the inventory analysis may include thesteps of: (1) calculating an in-store probability for each RFID tag inthe plurality based on the confidence probability curve and the time foreach RFID tag; and (2) summing the in-store probabilities. When acontra-EPC is encountered, however, the system may calculate aprobability for each contra-EPC based on the confidence probabilitycurve where the contra-EPC probabilities are subtracted from thesummation. In certain example embodiments where an exit RFID reader isused, the summation total may be configured to not include probabilitiesfor RFID tags detected by the exit RFID reader.

In various example embodiments the confidence probability curve may bebased on a variety of parameters, such as the following four parametersfor example: (1) P_(S)—the probability of an RFID tag from the pluralitybeing read in a day; (2) P_(D)—the probability of an RFID tag from theplurality that is unreadable becoming readable within a day; (3)P_(E)—the probability of an RFID tag from the plurality exiting thespace monitored by the RFID system without being detected by the RFIDsystem; and (4) I_(D)—the percentage of objects exiting the spacemonitored by the RFID system. As the system encounters and records moreinformation these probabilities may be refined and changed. A specificconfidence probability curve may be based on the following equation, forexample, where D is the number of days elapsed since the data signalfrom the RFID tag was received by the RFID system:

$\frac{( {1 - I_{D}} )^{D}( {1 - P_{S}} )( {1 - P_{D}} )^{D}}{{( {1 - I_{D}} )^{D}( {1 - P_{S}} )( {1 - P_{D}} )^{D}} + {P_{E}( {1 - ( {1 - I_{D}} )^{D}} )}}$

Various example methods of using confidence probability curves aredisclosed herein.

Further provided in various example embodiments are motion sensorsco-located with RFID readers. For example, a radio frequencyidentification (RFID) reader is disclosed that may contain a motiondetection sensor, a radio frequency transceiver configured to transmitRF energy at a first and second RFID profile, and a processor. Theprocessor may perform the following steps, for example: (1) when motionis detected by the sensor, commanding the transceiver to transmit RFenergy at the first RFID profile; and (2) when motion has not beendetect for a predetermined time period, commanding the transceiver totransmit RF energy at the second RFID profile. The motion sensor may bean optical, acoustic, infrared, sonar, or LIDAR sensor, for example.Such unique RFID readers may facilitate selective deep scans of RFIDtags.

For example, such unique readers may be deployed as part of largermulti-reader systems connected with a central controller. In variousexample embodiments, each such RFID reader may independently andselectively enter into a deep scan of the RFID tags. The RFID readersmay be merged with a point-of-sale (POS) system that records salesinformation. For example, the system may perform an inventory analysisof objects connected to the plurality of RFID tags based on theinformation detected by the RFID system, and optionally in conjunctionwith data from the POS system. Various example methods of employing suchselective deep scanning are disclosed herein.

Additional aspects, alternatives and variations as would be apparent topersons of skill in the art are also disclosed herein and arespecifically contemplated as included as part of the invention,including but not limited to all the embodiments shown or discussed inthe '637 application. The invention is set forth only in the claims asallowed by the patent office in this or related applications, and thefollowing summary descriptions of certain examples are not in any way tolimit, define or otherwise establish the scope of legal protection.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are depicted in the accompanying drawings forillustrative purposes, and should in no way be interpreted as limitingthe scope of the embodiments. Furthermore, various features of differentdisclosed embodiments can be combined to form additional embodiments,which are part of this disclosure. It will be understood that certaincomponents and details may not appear in the Figure(s) to assist in moreclearly describing example aspects of the invention.

FIG. 1 illustrates a facility with a Responsive Retail Sensor (RRS)system and point-of-sale (POS) system for managing inventory.

FIG. 2 illustrates a confidence probability curve with the parametersset to example default values.

FIG. 3A illustrates a confidence probability curve with P_(S) set at0.99, and the other parameters set to the example default values of FIG.2.

FIG. 3B illustrates a confidence probability curve with P_(S) set at0.01, and the other parameters set to the example default values of FIG.2.

FIG. 4A illustrates a confidence probability curve with P_(D) set at0.8, and the other parameters set to the example default values of FIG.2.

FIG. 4B illustrates a confidence probability curve with P_(D) set at0.01, and the other parameters set to the example default values of FIG.2.

FIG. 5A illustrates a confidence probability curve with P_(E) set at0.99, and the other parameters set to the example default values of FIG.2.

FIG. 5B illustrates a confidence probability curve with P_(E) set at0.01, and the other parameters set to the example default values of FIG.2.

FIG. 6A illustrates a confidence probability curve with I_(D) set at0.6, and the other parameters set to the example default values of FIG.2.

FIG. 6B illustrates a confidence probability curve with I_(D) set at0.1, and the other parameters set to the example default values of FIG.2.

FIG. 7 is a flow chart depicting example steps that may be used tocreate a contra-electronic product code (EPC).

FIG. 8 is a flow chart depicting example steps that may be used toarrive at an inventory count for a particular uniform product code(UPC).

FIG. 9 illustrates an example RFID reader with an example motion sensor.

FIG. 10 is a flow chart depicting the examples steps that may be used tooperate the RFID reader with a motion sensor of FIG. 9.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Reference is made herein to some specific examples of the presentinvention, including any best modes contemplated by the inventor(s) forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying figures. While the invention isdescribed in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed or illustrated embodiments. To the contrary, it is intended tocover alternatives, modifications, and equivalents as may be includedwithin the spirit and scope of the invention as defined by the appendedclaims.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of various aspects of thepresent invention. Particular example embodiments of the presentinvention may be implemented without some or all of these specificdetails. In other instances, process operations well known to persons ofskill in the art have not been described in detail in order not toobscure unnecessarily the present invention. Various techniques andmechanisms of the present invention will sometimes be described insingular form for clarity. However, it should be noted that someembodiments include multiple iterations of a technique or multiplemechanisms unless noted otherwise. Similarly, various steps of themethods shown and described herein are not necessarily performed in theorder indicated, or performed at all in certain embodiments.Accordingly, some implementations of the methods discussed herein mayinclude more or fewer steps than those shown or described. Further, thetechniques and mechanisms of the present invention will sometimesdescribe a connection, relationship or communication between two or moreentities. It should be noted that a connection or relationship betweenentities does not necessarily mean a direct, unimpeded connection, as avariety of other entities or processes may reside or occur between anytwo entities. Consequently, an indicated connection does not necessarilymean a direct, unimpeded connection unless otherwise noted.

The following list of example features corresponds with FIGS. 1-10 andis provided for ease of reference, where like reference numeralsdesignate corresponding features throughout the specification andfigures:

Facility—10;

RFID Readers—15;

RFID Exit Readers—20;

RFID Tag—25;

Point-of-sale (POS) optical scanner/register—30;

Controller—35;

Method for creating a contra-electronic product code (EPC)—700-750;

Method for arriving at an inventory count for a particular uniformproduct code (UPC)—800-850;

RFID reader with motion sensor—900;

Processor—905;

Motion Sensor—910;

RFID Transceiver—915; and

Method for changing RFID profiles based on detected motion—1000-1035.

Described herein is a probabilistic inventory application for an RFIDsystem that provides a more accurate prediction of a facility'sinventory. Also described herein is an improved system and method forthe deep scan of RFID tags throughout a facility, providing a moreaccurate inventory of the tagged items in the facility.

A. Probabilistic Inventory RFID Method and System

FIG. 1 illustrates a Responsive Retail Sensor (RRS) system that may beused within a facility 10. The RRS system may include a number of RFIDreaders 15 (also known as interrogators) placed throughout the facility10. By non-limiting example, these readers 15 can be overhead and canalso be handheld and mobile. A set of RFID exit readers 20 may be placedat an exit to the facility. Merchandise in the facility has physicallyconnected with it a passive RFID tag 25. The facility may also have apoint-of-sale (POS) system, for example at a check-out register, with anoptical scanner 30 that may read optical codes attached with themerchandise. The RRS and POS systems may coordinate via a controller 35,which may manage the various components to either improve the ability ofreading a tag or tags, or to avoid interference between the RFIDreaders, for example.

The readers 15, 20 can pulse a radio-frequency (RF) signal at the RFIDtag 25. The RF signal powers the RFID tag 25 and causes it to transmitstored data, which the readers 15, 20 can detect and read. The data inthis example embodiment is in the form of a unique electronic productcode (EPC). An RRS system can thereby yield a database of RFID tag readscontaining at least the following two pieces of information: (1) an EPC;and (2) when the EPC was last read.

An information table may be provided that associates each EPC to aparticular product. For example, the EPC may be associated with aparticular universal product code (UPC). UPCs are ubiquitous and arefound on most consumer items. A UPC may be in the form of a bar codethat an optical reader 30 may read. A UPC differs from an EPC in that aUPC is a category of product, while an EPC is a particular product. Forexample, the UPC associated with Levi's® Men's 501 Shrink To Fit Jeansis 052177361880. A particular store may have thirty pair of these jeanseach with the same UPC, whereas those jeans would have thirty differentEPCs unique to each pair.

When an RFID tag 25 is interrogated by a reader 15, 20, the tag 25 willideally report its EPC, which is then read by the reader 15, 20. Then bycross reference to the UPC/EPC table, the inventory management systemfor the facility will know the status of that merchandise—i.e., it isstill in the facility if it reported, or it presumably has left thefacility if it did not report.

While it is desirable to have all RFID tags associated with every itemreadable at all times, this is often impractical. The physics involvedwith the wireless link between the reader and tag, combined withcompeting needs for positioning the tags or items a particular way, aswell as the physical environment, often reduce the ability of thereaders to read tags as regularly as would be desired. Problems existwhen, for example, several RFID tags are present within the facility.Sending out an interrogation would likely result in several RFID tagsreporting simultaneously. It is common for some of those tags to be lostin the noise and not detected. The location of the tag can also causedetection problems. For example, the tag may be at the bottom of a pileof other merchandise and the tag may not receive the interrogationsignal. Or the tag may be under a metal shelf, forming a sort of Faradaycage that degrades the RF signal.

For any or all of the above reasons, a simple RRS system often missesseveral RFID tags and therefore cannot be relied on to form an accurateaccounting of the inventory within a facility. And without an accurateaccounting of the inventory, the facility would likely not run asefficiently as possible. A retail store, for example, would like to havea real-time accounting of its entire inventory so that it can order newmerchandise as necessary. Additionally, knowing minute-by-minuteinventory allows a retailer to predict trends for its merchandise andstock accordingly. A retailer can stock a larger variety of merchandiseif it knows what sells when. Alternatively, the retailer may maintain asmaller inventory if it chooses to focus its merchandising efforts ononly a few products. Ultimately, having an ongoing accurate picture ofcurrent inventory increases business efficiency, and simple RRS systemsfails to meet this objective.

Rather than focusing on the ability of a system to read all items in thestore, one example system and method disclosed herein uses data createdby an RRS system and other data sources (e.g., point-of-sale (POS) datafrom optical systems at the register) to determine when items enter orexit the space. Using data regarding entry detection and data regardingexit detection, the system can statistically determine the likelihood ofthe item being in the facility. For example, the system canstatistically determine the likelihood of the item being in the facilitybased on the last time it was read and whether it triggered an entranceor exit detection. Furthermore, the system can optionally learn thestatistical trends of entrance and exit detections to improve theaccuracy of the calculation of the likelihood the item is in the store.

Whereas traditional RRS systems provide a present or not-present flagfor the tag, the present system and method can use existing RRS systems,along with the information from the POS system, to provide a predictivemodel of the current inventory of merchandise in a facility. This allowsusers to have more accurate cumulative metrics. For instance, if thereare three items of the same type reported to be in a retail store, andit is calculated that each has a two-thirds likelihood of actually beingin the store, the best guess is that there are two items actually in thestore. A traditional approach would conclude that there are three itemsin the store. By allowing that retailer to make stock assessments basedon the statistically more accurate estimate of two versus the lessaccurate literal measurement of three can increase efficiency in storeoperations. The merchandiser may also extrapolate product merchandisingtrends using data and statistical analysis from this system.

In various example embodiments the system may provide a confidence thata particular RFID tag (and thus the merchandise connected with it) is inthe facility. The confidence is a probability (a value between 0.0 and1.0) that an item is in the facility. The predicted total number ofitems in the facility is the sum of their probabilities.

Each time something happens that can confirm an item is actually present(i.e. read by RFID reader within the store) the probability for that EPCis set to 1.0. If an exit event is recorded (i.e., read by the exit RFIDreader after a sale or consciously removed from inventory) then theprobability for that EPC is set to 0. It is between these two extremesthat the probabilistic methods described herein may be used. As timepasses without another confirmation of the item within the store, theprobability that it is still in the store decreases according to aconfidence probability curve that may be unique to the facility. Invarious example embodiments the confidence probability curve may bebased on at least the following five parameters:

-   -   P_(S)—Probability of item in store being read        -   May be determined from the daily read percentage, and be            updated continuously from day to day.        -   Range 0-1    -   P_(D)—Probability of an unreadable tag becoming readable each        day        -   What % of the items in the store are picked up and moved,            either by customers or store employees?        -   Can be learned continuously from look-backs        -   Range 0-1    -   P_(E)—Probability of exit error        -   What is the % of missed “departed” events        -   Can be learned from items that go unread for 30(TBR) days        -   Range 0-1    -   I_(D)—Daily inventory turn percentage        -   What % of the stores total items get sold each day?        -   Can be learned by netting out exit events            -   Total exit events+items unread for 30(TBR) days—return                events        -   Range 0-1    -   D—days since last read

For each item, the following equation may be used to define theprobability that the item is in inventory:Pr[in_store]=(1−I _(D))^(D)  (Eq. 1)Pr[not_read]=(1−P _(S))(1−P _(D))^(D)  (Eq. 2)Pr[out_of_store]=1−(1−I _(D))^(D)  (Eq. 3)Pr[left_undetected]=P _(E)  (Eq. 4)

A tag that has been unread for D days is either in the store and unreadfor D days (State A), or it left the store without triggering an exitevent (State B). So the likelihood that it is in the store is A/(A+B),which is the confidence probability curve:

$\begin{matrix}{{\Pr\lbrack {{in\_ store}❘{{{not\_ read}\mspace{14mu}\&}\mspace{14mu}{no\_ exit}{\_ event}}} \rbrack} = \frac{( {1 - I_{D}} )^{D}( {1 - P_{S}} )( {1 - P_{D}} )^{D}}{{( {1 - I_{D}} )^{D}( {1 - P_{S}} )( {1 - P_{D}} )^{D}} + {P_{E}( {1 - ( {1 - I_{D}} )^{D}} )}}} & ( {{Eq}.\mspace{14mu} 5} )\end{matrix}$

While this creates a robust confidence probability curve, a moreelementary curve may be constructed with some by not all of theparameters discussed above. Testing demonstrates that use of theseprobabilities provides a system that that is much more effective thanthe current present or not-present flag methodology.

The following example default values may be used to start the system:P_(S)=0.75; P_(D)=0.2; P_(E)=0.1; and I_(D)=0.01. Shown in FIG. 2 is agraphical representation of the confidence probability curve for a storewith these default variables. Considering the following conditions forthe parameters demonstrates and confirms the efficacy of Eq. 5.

If the overhead RFID readers could read 100% of the store every day, onewould expect the confidence to drop rapidly after not reading a tag fora couple days. If only 50% of the store's items are read by the overheadRFID readers each day, one would expect the confidence to not drop offnearly as fast. The effect on the confidence probability curve with theP_(S) parameter changed from 0.99 to 0.01, with all other variableremaining constant at the default values is shown in FIGS. 3A and 3B.

There are certain store activities that would make an otherwiseunreadable tag readable again. The more of a store's items that areinvolved in one of these activities each day, the more readable thosetags are. The confidence probability curve is affected in the same wayas with the P_(S) parameter. FIGS. 4A and 4B illustrate the effect onthe confidence probability curve with the P_(D) parameter changed from0.8 to 0.1, with all other variable remaining constant at the defaultvalues. The following actions may increase the P_(D) parameter: movingitems from one display to the next; taking an item into the fittingroom; retagging a known bad tag; and refolding and stacking merchandise(fluffing).

The number of missed departed events would have nearly the oppositeeffect. If there were never any missed departed events (i.e. 0%), thenyou would expect the confidence to remain high, even if you rarely reada tag. The effect on the confidence probability curve with the P_(E)parameter changed from 0.99 to 0.01, with all other variable remainingconstant at the default values is shown in FIGS. 5A and 5B.

When the daily inventory turn is high, then the consequence of notreading a tag for a few days is also high. It is reasonable to assumethat the tag has left the facility so one would expect the confidence todrop off more rapidly as time goes by. The effect on the confidenceprobability curve with the I_(D) parameter changed from 0.6 to 0.1, withall other variable remaining constant at the default values is shown inFIGS. 6A and 6B.

To determine the inventory of a particular UPC, the system would pullthe RRS records for the EPCs that are associated with the UPC. Forexample, a record for UPC 5656 could comprise the data shown in Table 1:

TABLE 1 DAYS SINCE LAST DETECTED AT EXIT READ FOR INSTORE RFID READER OREPC UPC RFID READER DEPARTED 00001 5656 1 N 00002 5656 11 N 00003 565629 N 00004 5656 2 N 00005 5656 3 Y

Using the confidence probability curve shown in FIG. 2, Table 2 liststhe in-store probabilities for the EPC's listed in Table 1:

TABLE 2 EPC PROBABILITY IN STORE 00001 1 00002 .65 00003 .01 00004 .9800005 0

Since EPC 0005 was detected at the exit RFID reader or departed (i.e.,it was sold or otherwise removed from inventory) the value is set to 0because it has left the store, regardless of the number of days it wasscanned by the in-store RFID readers. Summing the in-store probabilitiesyields a value of 2.64. Thus, the number of items with the UPC code of5656 in this example is predicted to be three. It is noteworthy that aprior art system would have reported an inventory of four because theRFID readers did read four EPCs with this associated UPC.

If instead of the default confidence probability curve, a facility hadan extremely high probability of reading all of the tags each day (i.e.,P_(S) is 0.99) such that the facility confidence probability curveactually followed FIG. 3A, then the individual in-store probabilitieswould be as shown in Table 3:

TABLE 3 EPC PROBABILITY IN STORE 00001 1 00002 .0685 00003 .0005 00004.98 00005 .75

Summing the in-store probabilities yields a value of 1.819. Thus thenumber of items with the UPC code of 5656 in this example is predictedto be two. It is noteworthy that the confidence probability curve decaysrapidly which is why the EPCs 00002 and 00003 with read days of 11 and29 are severely discounted in the system and do not affect in anynoticeably way the summed probability. This makes intuitive sensebecause the confidence probability curve is constructed with theassumption that 99% of the tags are read each day, so if the tags arenot read more than a few days, then the system has confidence that thetag is no longer in the facility.

Because the POS system uses an optical bar code reader to read the UPC,the POS system alone cannot determine which of the unique EPCs has beensold at any particular time. By merging the data from the POS system andthe RRS system, the facility can deduce which unique EPCs have actuallybeen sold and are no longer part of the inventory. For example, the POSsystem may detect that an item with a UPC of 5656 was sold, and within adefined period the RRS system may detect an EPC with an associated UPCof 5656 at an exit RFID reader. The unified POS/RRS system can thendeduce that the detected EPC was linked to the sold UPC, and the tablefor the unified POS/RRS system can be updated. The time between the POSscan and the RRS detection can vary. For example, the time may be a fewminutes at an establishment where the registers are near the exit, suchthat a purchaser would not normally loiter in the store afterconsummating a purchase. Or it may be longer at an establishment thathas several registers that are not located near the store exits, suchthat a user may purchase an item at one store register and then travelto a different department within the same store to purchase anotheritem. The time period may be set to have the highest confidence that alarge percentage of customer would have exited the store thusencountering the exit RFID reader, while simultaneously short enough toprovide up-to-date calculation of the current inventory.

When an item is not detected at the store exit within the defined periodof time, the unified POS/RRS system may assume that the item has indeedleft the store. However, that assumption would decay over a period ofdays; i.e., the inverse of the confidence probability curve. The POS/RRSsystem may therefore assign a contra-EPC to the particular UPC that wasdetected as sold by the POS but not detected as exiting the store by theRRS.

The contra-EPC is simply another probability that must be summed toarrive at a final probability that represents the number of a particularUPC that remains in inventory. The difference, however, is that thecontra-EPC has a negative probability. So taking the same exampleprovided above with reference to tables 1 and 2, a UPC without acorresponding EPC could comprise the data shown in Tables 4 and 5, withthe last entry being a contra-EPC:

TABLE 4 DAYS SINCE LAST DETECTED AT EXIT READ FOR INSTORE RFID READER OREPC UPC RFID READER DEPARTED 00001 5656 1 N 00002 5656 11 N 00003 565629 N 00004 5656 2 N 00005 5656 3 Y *** 5656 2 N

TABLE 5 EPC PROBABILITY IN STORE 00001 1 00002 .65 00003 .01 00004 .9800005 0 *** −.98

The contra-EPC indicates that an item was scanned at the POS two daysago, but not detected by the RRS exit RFID sensor. This results in asummed probability of 1.66, so two items with this UPC are predicted tobe in inventory. As time passes the contra-EPC will have less and lesseffect on the overall probability.

In alternative embodiments the POS system does not use an opticalscanner and instead scans the RFID tags to determine what has been soldat the register. If the POS system employs an RFID reader, then it wouldeffectively be similar to the exit RFID reader and once the EPC is read(and cross-referenced to the UPC and the sales price) that EPC can bemarked as sold and the tag database updated. In this scenario, it wouldnot be necessary to have a time interval from the POS reading to theexit RFID detection because there would be no need to associate an UPCwith an EPC, and therefore a contra-EPC would not be necessary.

Shown in FIG. 7 is a flow chart depicting a method 700 for creating acontra-EPC. At step 705 the sales record from the POS is queried and alist of departed EPCs with the allotted time is also queried from theRRS system. That information is distilled into a list, attempting tomatch a UPC to the EPCs at step 710. If the list is not empty at step715, then the first in the list of EPCs is marked as sold in steps 720and 725, and the tag database for the RRS is updated. If there arefurther entries on the list, then the system may mark those EPCs as soldand update the tag database at steps 730 and 735. This may occur, forexample, when a customer purchases multiple identical items, thus therewould be a POS record evidencing multiple sales of a particular UPC andalso multiple EPCs that have the same UPC associated with them. Ifhowever, the list is empty at step 715, the system then creates acontra-EPC that is associated with the same UPC at step 745, and the tagdatabase is updated with the contra-EPC (step 750). This method isrepeated for each UPC in the sales record. The method 700 is intended todiscover and account for a mismatch between the POS system recording asale of a particular UPC and the RRS system detecting an EPC with theparticular UPC.

Shown in FIG. 8 is a flow chart depicting a method 800 for arriving atan inventory count for a particular UPC. First a particular UPC isselected at step 805, and the tag database is queried to generate a listof EPCs that are associated with the same UPC (step 810). For each itemin the list, the confidence probability curve is consulted to calculatea probability based on the length of time from the last RFID reading(step 815). If the item in the list is a contra-EPC then the calculatedconfidence probability is subtracted from the running summation (step830). If it is not a contra-EPC then the calculated confidenceprobability is added to the summation (step 825). This is performed forevery item in the list (step 840), and the final summation is rounded tothe nearest integer and outputted (steps 845 and 850).

The parameters used in the confidence probability curve can be refinedby using past events. For example, P_(S) (the probability of item instore being read in a day) may begin at 0.75 but over several days thesystem may determine that more than 75% of the known inventory has alast read date of a single day. In this case, the P_(S) should beincreased to more accurately match the data found in the system'sdatabase. Likewise P_(D) (the probability of an unreadable tag becomingreadable each day) can be refined by looking at the last read dates forblocks of EPCs. If, for example, a representative block of EPCs remainsunreadable for a maximum of five days but on average the EPCs in theblock are read every three days, then the default of 0.2 should beincreased. Finally, if the system is creating several contra-EPCs at arate of more than the currently set P_(E) (the probability of exiterror), then the P_(E) should be increased. Making these refinementsincreases the accuracy of the system.

Utilizing the disclosed method and system has the added benefit ofpotentially identifying deficiencies in the existing RRS system. Forexample, a high contra-EPC count may indicate that the exit RFID sensorsare misaligned or misconfigured, or the tags have other issues thatplace them out of range. Correcting this issue would tend to reduce theP_(E). As a matter of practice, facilities often perform a hand RFIDcount and or a physical hand count. A high and continued discrepancybetween the hand RFID or physical hand count and the inventory count mayindicate that the overhead RFID readers are misaligned or misconfigured,or that the existing number of RFID readers is unable to cover theentire facility, resulting in “hidden” merchandise. Correcting thisissue would tend to increase the P_(S) and P_(D).

By refining the parameters used in the confidence probability curve andcorrecting any deficiencies in the existing RRS system, the system canprovide ever more accurate predictions of the current inventory withinthe facility. Not only does this assist in more efficient merchandisingas discussed herein, it also can significantly reduce the need forperiodic RFID scanning by hand.

B. Method and Apparatus for Selective Deep Scan Using A Motion SensorCo-Located with RFID Reader

RRS system may use an RFID tag that has different response times basedon the RF signal that is interrogating it. This type of RRS systemallows for a deep scan of the inventory, yielding more accuratereadings.

Consider for example a stack of clothing that is being interrogated byRFID. The topmost item will likely be the most detectable to a ceilingmounted RFID reader, while the bottommost item may not be heard over thenoise of all the items above it. In a deep scan, the RFID reader sends acontinuous signal at a particular RFID profile for a period of time(e.g., for ten minutes) that attempts to elicit responses from all tagscorresponding to that signal. But the tags are designed to go silent fora period of time after each sends their response (for instance for aperiod of one minute). Thus, the topmost items might report first andthen go quiet (e.g., for a minute), allowing other items, such as thebottommost items, to be interrogated and detected by the readers. Whilethe above discussion is simplified, in general this process is known asa deep scan.

Most facilities will perform a deep scan when the facility is closedbecause the inventory is not moving, such that the long response timewill not adversely affect the readings with missed tags. But when thefacility is operational, and items are moving, the RFID readers willchange to an RFID profile that elicits a response from the tags at amuch higher rate (e.g. 1 second, 0.5 seconds). When items are movingthey are easier to detect. Nevertheless, at this higher rate the topmosttags may drown out the reporting signal from the bottommost tags. Thisis an example of a mobility scan.

In general, an effective mobility scan profile will allow any oneparticular tag to be read as often as possible. Reading a tag morefrequently facilitates better tracking of the tag as it moves throughoutthe store. Some of the parameters that can be adjusted to support thisare:

-   -   1. A modulation that supports a higher data transfer rate. In        general, this is less robust. However, an RFID tag in motion is        easier to read. Also, more frequent reads means that some of        them can be dropped due to errors without adversely affecting        the data collection process.    -   2. Using a session with a short persistence value. Querying the        state of a session flag that has a short (or no) persistence        value will that cause the tag to respond more frequently than        querying the state of a session flag that has a long persistence        value.    -   3. Using as small of a “Q” value as possible. Tags respond to        the interrogator in a time-slotted fashion. The more time slots        there are in a time epoch, the longer the period of time between        opportunities for a tag to respond. The use of dynamic “Q”        automatically chooses the smallest “Q” value possible.    -   4. Toggle the state of the session flag after each interrogation        round. This allows all tags to participate in every        interrogation round.

In general, an effective deep scan profile will read each tag capable ofhearing the interrogator to respond only once. Suppressing multipleresponses from a single tag facilitates a deeper read by allowing thosetags with weaker responses to be heard by the interrogator. Some of theparameters that can be adjusted to support this are:

-   -   1. A modulation that supports a slower data transfer rate. In        general, this is more robust. Since each tag is not read very        often, it is important for that data transfer to be as robust as        possible.    -   2. Using a session with a long persistence value. Querying the        state of a session flag that has a long persistence value will        cause the tag to respond once and suppress further responses.    -   3. Using as large of a “Q” value as possible. Tags respond to        the interrogator in a time-slotted fashion. The more time slots        there are in a time epoch, the fewer collisions there are        between tags and the higher likelihood of the interrogator        receiving the response. The use of fixed “Q” assures the “Q”        value does not change.    -   4. Do not toggle the state of the session flag after each        interrogation round. Once a tag responds to its interrogator, it        will remains silent for as long as a tag is energized with RF        (i.e. the end of the interrogation round). This allows all        possible tags to respond at least once during interrogation        round.

A sample value matrix for a mobility scan profile and a deep scanprofile is provided in the Appendix.

FIG. 9 depicts an RFID reader 900 with a processor 905 connected to amotion sensor 910 and the RFID transceiver 915. When the motion sensor910 detects movement it reports this to the processor 905, which thensends instruction to the RFID transceiver 915 to emit RF energy to thetags at an RFID profile that will elicit responses from the tags at thehigher rate (i.e., the mobility scan mode). Conversely, when no movementis detected, the processor 905 directs the RFID transceiver to emit RFenergy to the tags at an RFID profile that will elicit responses fromthe tags at the lower but more robust rate (i.e., the deep scan mode).The system may use a predefined period between detected movement and nomovement before activating the deep scan mode. If the facility hasmultiple RFID readers, some may be in deep scan mode while others are innon-deep scan mode, depending on the movement detected by the particularRFID reader. The motion sensor may be, for example, an optical, anacoustic, an infrared, a sonar, or a LIDAR sensor, or any other suitabletype of sensor.

FIG. 10 is a flow chart depicting a method 1000 for method for changingRFID profiles based on detected motion. First at step 1005 the motionsensor data is collected and then assessed at step 1010. If motion isdetected the RFID profile is set to mobility scan (step 1015) and thetime counter is reset. The system then returns to collecting andassessing the motion sensor data. If however, no motion is detected thenthe time counter is increased at step 1025. If no motion has beendetected for a long enough period (i.e., the time counter exceeds apredetermined threshold, step 1030) then the RFID profile is set to deepscan at step 1035.

The motion detecting RFID readers may be part of larger RRS and POSsystems, for instance like those described herein. Permitting deepscanning during operational hours of the facility conveniently providesan overall more accurate reading of the RFID tags within the facility.This yields a more accurate probabilistic inventory model because moreof the RFID tags are engaged by the process.

The above description of the disclosed example embodiments is providedto enable persons skilled in the art to make and use the invention.Various modifications to these example embodiments will be readilyapparent to those skilled in the art, and the generic principlesdescribed herein can be applied to other example embodiments withoutdeparting from the spirit or scope of the invention. Thus, it is to beunderstood that the description and drawings presented herein representa presently preferred example embodiment of the invention and aretherefore representative of the subject matter which is broadlycontemplated by the present invention. It is further understood that thescope of the present invention fully encompasses other exampleembodiments that may become apparent to those skilled in the art. Thescope of the present invention is accordingly limited by nothing otherthan the appended claims.

APPENDIX

The foregoing disclosure may use various RFID profiles. The following isthe value matrix for a Mobility and Deep Scan Profiles:

MOBILITY DEEP SCAN PROFILE PROFILE LINK_PROFILE 2 or 3 0 or 1SESSION_FLAG S0 or S1 S2 or S3 TARGET_STATE Any Any Q_ALGORITHM DynamicFixed FIXED_Q_VALUE N/A 12 START_Q_VALUE  4 N/A MIN_Q_VALUE  4 N/AMAX_Q_VALUE 15 N/A TOGGLE_TARGET_FLAG true false REPEAT_UNTIL_NO_TAGSfalse trueThe values are defined as follows:

-   -   LINK_PROFILE specifies the RF physical layer parameters used for        the next tag protocol operation. In general, the higher the        Rate, the less robust. Definitions for the valid profiles are        shown in the following chart:

Link Profile 0 1 2 3 Modulation DSB-ASK PR-ASK PR-ASK DSB-ASK Tari (US)25 25 25 6.25 Data 1 .05 0.5 0.5 Width (US) 12.5 12.5 12.5 3.13 R-T Calc(US) 75 62.5 62.5 15.63 T-R Calc (US) 200 85.33 71.11 20 Divide Ratio 821.33 21.33 8 Encoding FM0 Miller-4 Miller-4 FM0 Pilot Tone 1 1 1 1 LinkF (kHz) 40 250 300 400 Rate (kbps) 40 62.5 75 400

-   -   SESSION_FLAG specifies which inventory session flag is matched        against the inventory state specified by TARGET_STATE. Valid        values are: S0, S1, S2, S3    -   TARGET_STATE specifies the state of the inventory session flag        specified by SESSION_FLAG, for tags that are to have the        operation applied to them. Valid values are: A, B, Any    -   The Q (Query) value used to specify the number of time slots        that a tag can respond in. Slots=2Q−1. There are several Q        values specified: FIXED_Q_VALUE, START_Q_VALUE (used with        Dynamic Q Algorithm), MIN_Q_VALUE (used with Dynamic Q        Algorithm), MAX_Q_VALUE (used with Dynamic Q Algorithm). Valid        values are: 0-15    -   TOGGLE_TARGET_FLAG specifies whether or not, after performing        the inventory cycle for the specified target (i.e. A or B), the        target should be toggled (i.e. A to B or B to A) and another        inventory cycle should be run. Valid values are: false, true    -   REPEAT_UNTIL_NO_TAGS specifies whether or not the singulation        algorithm should continue performing inventory rounds until no        tags are singulated. Valid values are: false, true

For further disclosure regarding these values, the reader may referencethe EPC™ Radio-Frequency Identity Protocols Generation-2 UHF RFID;Specification for RFID Air Interface; Protocol for Communications at 860MHz-960 MHz; Version 2.0.0 Ratified, which is incorporated herein byreference. As of the date of the filing of this application, thisreference could be downloaded fromhttp://www.gs1.org/sites/default/files/docs/epc/uhfc1g2_2_0_0_standard_20131101.pdf.

The invention claimed is:
 1. A radio frequency identification (RFID)reader comprising: a motion detection sensor; a radio frequency (RF)transceiver to transmit RF energy at a first and a second RFID profile;and a processor connected to the motion detection sensor and the RFtransceiver, the processor to: when the motion detection sensor detectsmotion, instruct the transceiver to transmit the RF energy at the firstRFID profile corresponding to a dynamic number of time slots in which anRFID tag can respond; and when the motion detection sensor has notdetected motion for a predetermined time period, instruct thetransceiver to transmit the RF energy at the second RFID profilecorresponding to a fixed number of time slots in which the RFID tag canrespond.
 2. The RFID reader of claim 1, wherein the motion detectionsensor includes at least one of an optical sensor, an acoustic sensor,an infrared sensor, a sonar sensor, or a LIDAR sensor.
 3. The RFIDreader of claim 1, wherein the second RFID profile is to cause a deepscan of a plurality of RFID tags.
 4. A radio frequency identification(RFID) system comprising: a controller connected to a plurality of RFIDreaders, respective RFID readers including: a motion detection sensor; aradio frequency (RF) transceiver to transmit RF energy at a first and asecond RFID profile; and a processor connected to the motion detectionsensor and the RF transceiver, the processor to: when the motiondetection sensor detects motion, instruct the transceiver to transmitthe RF energy at the first RFID profile corresponding to a dynamicnumber of time slots in which an RFID tag can respond; and when themotion detection sensor has not detected motion for a predetermined timeperiod, instruct the transceiver to transmit the RF energy at the secondRFID profile corresponding to a fixed number of time slots in which theRFID tag can respond.
 5. The system of claim 4, wherein the motiondetection sensor includes at least one of an optical sensor, an acousticsensor, an infrared sensor, a sonar sensor, or a LIDAR sensor.
 6. Thesystem of claim 4 further including a plurality of RFID tags, respectiveones of the RFID tags to transmit data at a first time interval when therespective tag receives the RF energy at the first RFID profile, and ata second time interval when the respective tag receives the RF energy atthe second RFID profile.
 7. The system of claim 6, wherein the pluralityof RFID tags are to transmit data for the second time interval for alonger duration than the first interval.
 8. The system of claim 4,wherein the RF transceiver in the respective readers is to detect datasignals from a plurality of RFID tags, the system further including adatabase operatively connected with the controller, the database torecord a time when the data signals are received from respective tags inthe plurality of RFID tags, data contained in the data signals fromrespective tags in the plurality of RFID tags.
 9. The system of claim 8,wherein the data signals include an electronic product code (EPC). 10.The system of claim 8 wherein respective ones of the RFID tags areconnected with an object, the system further including a point-sale-sale(POS) system that is to record sales information for the object in thedatabase.
 11. The system of claim 8, wherein the controller includes asecond processor to perform an inventory analysis of objects connectedto the plurality of RFID tags.
 12. A method for selectively inducing adeep scan in a radio frequency identification (RFID) system monitoring aspace, the system including a controller connected to a plurality ofRFID readers, wherein respective RFID readers include a motion detectionsensor, and a radio frequency (RF) transceiver to transmit RF energy ata first and a second RFID profile, the method comprising: when themotion detection sensor detects motion, instructing the transceiver totransmit the RF energy at the first RFID profile corresponding to adynamic number of time slots in which an RFID tag can respond; and whenthe motion detection sensor has not detected motion for a predeterminedtime period, instructing the transceiver to transmit the RF energy atthe second RFID profile corresponding to a fixed number of time slots inwhich the RFID tag can respond.
 13. The method of claim 12, wherein theRF transceiver in respective readers is to detect data signals from aplurality of RFID tags, the method further including: recording a timewhen the data signal is received from respective tags in the pluralityof RFID tags; and recording the data contained in the data signal fromthe respective tags in the plurality of RFID tags.
 14. The method ofclaim 12, further including performing an inventory analysis of objectsconnected with the plurality of RFID tags.